1 citations · 1 across the 4 of their papers we have counts for
4 papers
URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios
Giuseppe Spillo, Zixuan Yi, Aleksandr Petrov +3
Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represent…
Eco-Amazon: Enriching E-commerce Datasets with Product Carbon Footprint for Sustainable Recommendations
Giuseppe Spillo, Allegra De Filippo, Cataldo Musto +2
In the era of responsible and sustainable AI, information retrieval and recommender systems must expand their scope beyond traditional accuracy metrics to incorporate environmental…
Binge Watch: Reproducible Multimodal Benchmarks Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M
Giuseppe Spillo, Alessandro Petruzzelli, Cataldo Musto +3
As Multimodal Recommender Systems gain interest, high-quality datasets with multimedia side information have become essential. However, much of the current literature reports exper…
The URW-KG: a Resource for Tackling the Underrepresentation of non-Western Writers
Marco Antonio Stranisci, Giuseppe Spillo, Cataldo Musto +2
Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information abo…